import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")
%matplotlib inline

df=pd.read_csv('marketing_campaign.csv')
print(df)

         ID  Year_Birth   Education Marital_Status   Income  Kidhome  \
0      5524        1957  Graduation         Single  58138.0        0   
1      2174        1954  Graduation         Single  46344.0        1   
2      4141        1965  Graduation       Together  71613.0        0   
3      6182        1984  Graduation       Together  26646.0        1   
4      5324        1981         PhD        Married  58293.0        1   
...     ...         ...         ...            ...      ...      ...   
2235  10870        1967  Graduation        Married  61223.0        0   
2236   4001        1946         PhD       Together  64014.0        2   
2237   7270        1981  Graduation       Divorced  56981.0        0   
2238   8235        1956      Master       Together  69245.0        0   
2239   9405        1954         PhD        Married  52869.0        1   

      Teenhome Dt_Customer  Recency  MntWines  ...  NumWebVisitsMonth  \
0            0    4/9/2012       58       635  ...                  7   
1            1    8/3/2014       38        11  ...                  5   
2            0  21-08-2013       26       426  ...                  4   
3            0   10/2/2014       26        11  ...                  6   
4            0  19-01-2014       94       173  ...                  5   
...        ...         ...      ...       ...  ...                ...   
2235         1  13-06-2013       46       709  ...                  5   
2236         1   10/6/2014       56       406  ...                  7   
2237         0  25-01-2014       91       908  ...                  6   
2238         1  24-01-2014        8       428  ...                  3   
2239         1  15-10-2012       40        84  ...                  7   

      AcceptedCmp3  AcceptedCmp4  AcceptedCmp5  AcceptedCmp1  AcceptedCmp2  \
0                0             0             0             0             0   
1                0             0             0             0             0   
2                0             0             0             0             0   
3                0             0             0             0             0   
4                0             0             0             0             0   
...            ...           ...           ...           ...           ...   
2235             0             0             0             0             0   
2236             0             0             0             1             0   
2237             0             1             0             0             0   
2238             0             0             0             0             0   
2239             0             0             0             0             0   

      Complain  Z_CostContact  Z_Revenue  Response  
0            0              3         11         1  
1            0              3         11         0  
2            0              3         11         0  
3            0              3         11         0  
4            0              3         11         0  
...        ...            ...        ...       ...  
2235         0              3         11         0  
2236         0              3         11         0  
2237         0              3         11         0  
2238         0              3         11         0  
2239         0              3         11         1  

[2240 rows x 29 columns]

df.head()

df.tail()

df.shape

(2240, 29)

df.isnull().sum()

ID                      0
Year_Birth              0
Education               0
Marital_Status          0
Income                 24
Kidhome                 0
Teenhome                0
Dt_Customer             0
Recency                 0
MntWines                0
MntFruits               0
MntMeatProducts         0
MntFishProducts         0
MntSweetProducts        0
MntGoldProds            0
NumDealsPurchases       0
NumWebPurchases         0
NumCatalogPurchases     0
NumStorePurchases       0
NumWebVisitsMonth       0
AcceptedCmp3            0
AcceptedCmp4            0
AcceptedCmp5            0
AcceptedCmp1            0
AcceptedCmp2            0
Complain                0
Z_CostContact           0
Z_Revenue               0
Response                0
dtype: int64

df.dropna(inplace=True)

df.isnull().sum()

ID                     0
Year_Birth             0
Education              0
Marital_Status         0
Income                 0
Kidhome                0
Teenhome               0
Dt_Customer            0
Recency                0
MntWines               0
MntFruits              0
MntMeatProducts        0
MntFishProducts        0
MntSweetProducts       0
MntGoldProds           0
NumDealsPurchases      0
NumWebPurchases        0
NumCatalogPurchases    0
NumStorePurchases      0
NumWebVisitsMonth      0
AcceptedCmp3           0
AcceptedCmp4           0
AcceptedCmp5           0
AcceptedCmp1           0
AcceptedCmp2           0
Complain               0
Z_CostContact          0
Z_Revenue              0
Response               0
dtype: int64

df

print(df.duplicated().sum())

0

df.columns

Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',
       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',
       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2', 'Complain', 'Z_CostContact', 'Z_Revenue', 'Response'],
      dtype='object')

df.columns=df.columns.str.strip()
df

df.dtypes

ID                       int64
Year_Birth               int64
Education               object
Marital_Status          object
Income                 float64
Kidhome                  int64
Teenhome                 int64
Dt_Customer             object
Recency                  int64
MntWines                 int64
MntFruits                int64
MntMeatProducts          int64
MntFishProducts          int64
MntSweetProducts         int64
MntGoldProds             int64
NumDealsPurchases        int64
NumWebPurchases          int64
NumCatalogPurchases      int64
NumStorePurchases        int64
NumWebVisitsMonth        int64
AcceptedCmp3             int64
AcceptedCmp4             int64
AcceptedCmp5             int64
AcceptedCmp1             int64
AcceptedCmp2             int64
Complain                 int64
Z_CostContact            int64
Z_Revenue                int64
Response                 int64
dtype: object

df.info()

<class 'pandas.core.frame.DataFrame'>
Index: 2216 entries, 0 to 2239
Data columns (total 29 columns):
 #   Column               Non-Null Count  Dtype  
---  ------               --------------  -----  
 0   ID                   2216 non-null   int64  
 1   Year_Birth           2216 non-null   int64  
 2   Education            2216 non-null   object 
 3   Marital_Status       2216 non-null   object 
 4   Income               2216 non-null   float64
 5   Kidhome              2216 non-null   int64  
 6   Teenhome             2216 non-null   int64  
 7   Dt_Customer          2216 non-null   object 
 8   Recency              2216 non-null   int64  
 9   MntWines             2216 non-null   int64  
 10  MntFruits            2216 non-null   int64  
 11  MntMeatProducts      2216 non-null   int64  
 12  MntFishProducts      2216 non-null   int64  
 13  MntSweetProducts     2216 non-null   int64  
 14  MntGoldProds         2216 non-null   int64  
 15  NumDealsPurchases    2216 non-null   int64  
 16  NumWebPurchases      2216 non-null   int64  
 17  NumCatalogPurchases  2216 non-null   int64  
 18  NumStorePurchases    2216 non-null   int64  
 19  NumWebVisitsMonth    2216 non-null   int64  
 20  AcceptedCmp3         2216 non-null   int64  
 21  AcceptedCmp4         2216 non-null   int64  
 22  AcceptedCmp5         2216 non-null   int64  
 23  AcceptedCmp1         2216 non-null   int64  
 24  AcceptedCmp2         2216 non-null   int64  
 25  Complain             2216 non-null   int64  
 26  Z_CostContact        2216 non-null   int64  
 27  Z_Revenue            2216 non-null   int64  
 28  Response             2216 non-null   int64  
dtypes: float64(1), int64(25), object(3)
memory usage: 519.4+ KB

df.describe()

df.drop(['Z_CostContact', 'Z_Revenue'], axis=1, inplace=True)

df

df['Age']=2024-df['Year_Birth']
df

df['Dt_Customer'] = pd.to_datetime(df['Dt_Customer'], format='mixed')

df

df.value_counts('Education')

Education
Graduation    1116
PhD            481
Master         365
2n Cycle       200
Basic           54
Name: count, dtype: int64

plt.figure(figsize=(14,7))
sns.countplot(x='Education',data=df)
plt.xlabel('Education')
plt.show()

fig = px.bar(df, x = 'Education', y = "Income", color = "Education", title = "Education vs Income")
fig.update_layout(xaxis = {'categoryorder':'total descending'})
fig.show()

df.value_counts('Marital_Status')

Marital_Status
Married     857
Together    573
Single      471
Divorced    232
Widow        76
Alone         3
Absurd        2
YOLO          2
Name: count, dtype: int64

plt.figure(figsize=(14,7))
sns.countplot(x='Marital_Status',data=df)
plt.xlabel('Marital_Status')
plt.show()

fig = px.bar(df, x = 'Marital_Status', y = "Income", color = "Marital_Status", title = "Marital_Status vs Income")
fig.update_layout(xaxis = {'categoryorder':'total descending'})
fig.show()

df.value_counts('AcceptedCmp1')

AcceptedCmp1
0    2074
1     142
Name: count, dtype: int64

df.value_counts('AcceptedCmp2')

AcceptedCmp2
0    2186
1      30
Name: count, dtype: int64

df.value_counts('AcceptedCmp3')

AcceptedCmp3
0    2053
1     163
Name: count, dtype: int64

df.value_counts('AcceptedCmp4')

AcceptedCmp4
0    2052
1     164
Name: count, dtype: int64

df.value_counts('AcceptedCmp5')

AcceptedCmp5
0    2054
1     162
Name: count, dtype: int64

df.value_counts('Response')

Response
0    1883
1     333
Name: count, dtype: int64

df.groupby("Marital_Status")["Recency"].sum().plot(kind = 'pie' ,autopct='%1.1f%%')
plt.title('Distribution of Recency by Marital Status')
plt.show()

plt.figure(figsize=(14,7))
sns.countplot(x='Kidhome',data=df,palette='Set1')
plt.xlabel('Kidhome')
plt.show()

plt.figure(figsize=(14,7))
sns.countplot(x='Teenhome',data=df, palette="Set2")
plt.xlabel('Teenhome')
plt.show()

df = df[df['Age'] <= 100]
df

age_counts=df['Age'].value_counts().sort_values(ascending=False)
plt.figure(figsize=(14,7))
sns.countplot(x='Age',data=df,order=age_counts.index)
plt.xlabel('Age')
plt.xticks(rotation=90)
plt.show()

sns.scatterplot(x='Age' ,y= 'Recency' ,data=df)
plt.title("Scatter Plot of Age vs. Recency")
plt.show()

plt.figure(figsize=(14,7))
sns.countplot(x='NumDealsPurchases',data=df)
plt.xlabel('NumDealsPurchases')
plt.show()

plt.figure(figsize=(14,7))
sns.countplot(x='NumStorePurchases',data=df)
plt.xlabel('NumStorePurchases')
plt.show()

plt.figure(figsize=(14,7))
sns.countplot(x='NumWebPurchases',data=df)
plt.xlabel('NumWebPurchases')
plt.show()

plt.figure(figsize=(14,7))
sns.countplot(x='NumCatalogPurchases',data=df)
plt.xlabel('NumCatalogPurchases')
plt.show()

plt.figure(figsize=(14,7))
sns.barplot(x='Marital_Status',y='NumStorePurchases',data=df, errorbar=('ci',0))
plt.xlabel('Marital_Status')
plt.ylabel('NumStorePurchases')
plt.title('Marital_Status vs NumStorePurchases')
plt.xticks(rotation=45)
plt.show()

plt.figure(figsize=(14,7))
sns.barplot(x='Marital_Status',y='NumWebPurchases',data=df, errorbar=('ci',0))
plt.xlabel('Marital_Status')
plt.ylabel('NumWebPurchases')
plt.title('Marital_Status vs NumWebPurchases')
plt.xticks(rotation=45)
plt.show()

plt.figure(figsize=(14,7))
sns.barplot(x='Marital_Status',y='NumCatalogPurchases',data=df, errorbar=('ci',0))
plt.xlabel('Marital_Status')
plt.ylabel('NumCatalogPurchases')
plt.title('Marital_Status vs NumCatalogPurchases')
plt.xticks(rotation=45)
plt.show()

plt.figure(figsize=(14,7))
sns.barplot(x='Marital_Status',y='NumWebVisitsMonth',data=df, errorbar=('ci',0))
plt.xlabel('Marital_Status')
plt.ylabel('NumWebVisitsMonth')
plt.title('Marital_Status vs NumWebVisitsMonth')
plt.xticks(rotation=45)
plt.show()

total_sum = df["NumDealsPurchases"].sum()
print(f"Total sum of NumDealsPurchases: {total_sum}")

Total sum of NumDealsPurchases: 5146

total_sum = df["NumWebPurchases"].sum()
print(f"Total sum of NumWebPurchases: {total_sum}")

Total sum of NumWebPurchases: 9046

total_sum = df["NumCatalogPurchases"].sum()
print(f"Total sum of NumCatalogPurchases: {total_sum}")

Total sum of NumCatalogPurchases: 5912

total_sum = df["NumStorePurchases"].sum()
print(f"Total sum of NumStorePurchases: {total_sum}")

Total sum of NumStorePurchases: 12847

total_sum = df["NumWebVisitsMonth"].sum()
print(f"Total sum of NumWebVisitsMonth: {total_sum}")

Total sum of NumWebVisitsMonth: 11777

data = df.groupby("Marital_Status")["MntMeatProducts"].sum().reset_index()
fig = px.pie(data, values='MntMeatProducts', names='Marital_Status', title='Meat Products Sum by Marital Status')
fig.show()

data = df.groupby("Marital_Status")["MntFruits"].sum().reset_index()
fig = px.pie(data, values='MntFruits', names='Marital_Status', title='Fruit Products Sum by Marital Status')
fig.show()

data = df.groupby("Marital_Status")["MntWines"].sum().reset_index()
fig = px.pie(data, values='MntWines', names='Marital_Status', title='wine Products Sum by Marital Status')
fig.show()

data = df.groupby("Marital_Status")["MntFishProducts"].sum().reset_index()
fig = px.pie(data, values='MntFishProducts', names='Marital_Status', title='Fish Products Sum by Marital Status')
fig.show()

data = df.groupby("Marital_Status")["MntSweetProducts"].sum().reset_index()
fig = px.pie(data, values='MntSweetProducts', names='Marital_Status', title='Sweet Products Sum by Marital Status')
fig.show()

data = df.groupby("Marital_Status")["MntGoldProds"].sum().reset_index()
fig = px.pie(data, values='MntGoldProds', names='Marital_Status', title='Gold Products Sum by Marital Status')
fig.show()

correlation = df[['Income','Kidhome','Teenhome','Recency','MntWines','MntFruits','MntMeatProducts','MntFishProducts','MntSweetProducts',
           'MntGoldProds','NumDealsPurchases','NumWebPurchases','NumCatalogPurchases','NumStorePurchases','NumWebVisitsMonth']].corr()
plt.figure(figsize=(10,8))
sns.heatmap(correlation, cmap='coolwarm')
for i in range(correlation.shape[0]):
    for j in range(correlation.shape[1]):
        plt.text(j+0.5, i+0.5, "{:.2f}".format(correlation.iloc[i, j]), ha='center', va='center', color='white')
plt.title('Correlation Heatmap')
plt.show()

fig = px.sunburst(df, path=['Education', 'Marital_Status'], values='Income', title='Sunburst Plot of Income by Education and Marital Status')
fig.show()

complain_counts = df['Complain'].value_counts()
plt.figure(figsize=(8,8))
complain_counts.plot(kind='pie', autopct='%1.1f%%')
plt.title('Distribution of Complains')
plt.ylabel('')
plt.show()

df.value_counts('Complain')

Complain
0    2193
1      20
Name: count, dtype: int64

df.groupby("Education")["Complain"].sum().plot(kind = 'pie' ,autopct='%1.1f%%')
plt.title('Distribution of Complaints by Education')
plt.ylabel('')
plt.show()

df.groupby("Marital_Status")["Complain"].sum().plot(kind = 'pie' ,autopct='%1.1f%%')
plt.title('Distribution of Complaints by Marital_Status')
plt.show()

df.columns

Index(['ID', 'Year_Birth', 'Education', 'Marital_Status', 'Income', 'Kidhome',
       'Teenhome', 'Dt_Customer', 'Recency', 'MntWines', 'MntFruits',
       'MntMeatProducts', 'MntFishProducts', 'MntSweetProducts',
       'MntGoldProds', 'NumDealsPurchases', 'NumWebPurchases',
       'NumCatalogPurchases', 'NumStorePurchases', 'NumWebVisitsMonth',
       'AcceptedCmp3', 'AcceptedCmp4', 'AcceptedCmp5', 'AcceptedCmp1',
       'AcceptedCmp2', 'Complain', 'Response', 'Age'],
      dtype='object')

total_sum = df["MntWines"].sum()
print(f"Total sum of MntWines: {total_sum}")

Total sum of MntWines: 675305

total_sum = df["MntFruits"].sum()
print(f"Total sum of MntFruits: {total_sum}")

Total sum of MntFruits: 58255

total_sum = df["MntMeatProducts"].sum()
print(f"Total sum of MntMeatProducts: {total_sum}")

Total sum of MntMeatProducts: 369488

total_sum = df["MntFishProducts"].sum()
print(f"Total sum of MntFishProducts: {total_sum}")

Total sum of MntFishProducts: 83287

total_sum = df["MntSweetProducts"].sum()
print(f"Total sum of MntSweetProducts: {total_sum}")

Total sum of MntSweetProducts: 59828

total_sum = df["MntGoldProds"].sum()
print(f"Total sum of MntGoldProds: {total_sum}")

Total sum of MntGoldProds: 97176

	ID	Year_Birth	Income	Kidhome	Teenhome	Recency	MntWines	MntFruits	MntMeatProducts	MntFishProducts	...	NumWebVisitsMonth	AcceptedCmp3	AcceptedCmp4	AcceptedCmp5	AcceptedCmp1	AcceptedCmp2	Complain	Z_CostContact	Z_Revenue	Response
count	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	...	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.000000	2216.0	2216.0	2216.000000
mean	5588.353339	1968.820397	52247.251354	0.441787	0.505415	49.012635	305.091606	26.356047	166.995939	37.637635	...	5.319043	0.073556	0.074007	0.073105	0.064079	0.013538	0.009477	3.0	11.0	0.150271
std	3249.376275	11.985554	25173.076661	0.536896	0.544181	28.948352	337.327920	39.793917	224.283273	54.752082	...	2.425359	0.261106	0.261842	0.260367	0.244950	0.115588	0.096907	0.0	0.0	0.357417
min	0.000000	1893.000000	1730.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
25%	2814.750000	1959.000000	35303.000000	0.000000	0.000000	24.000000	24.000000	2.000000	16.000000	3.000000	...	3.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
50%	5458.500000	1970.000000	51381.500000	0.000000	0.000000	49.000000	174.500000	8.000000	68.000000	12.000000	...	6.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
75%	8421.750000	1977.000000	68522.000000	1.000000	1.000000	74.000000	505.000000	33.000000	232.250000	50.000000	...	7.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	3.0	11.0	0.000000
max	11191.000000	1996.000000	666666.000000	2.000000	2.000000	99.000000	1493.000000	199.000000	1725.000000	259.000000	...	20.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	3.0	11.0	1.000000

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumWebVisitsMonth	Z_CostContact	Z_Revenue	Response
0	5524	1957	Graduation	Single	58138.0	0	0	4/9/2012	58	635	...	7	3	11	1
1	2174	1954	Graduation	Single	46344.0	1	1	8/3/2014	38	11	...	5	3	11	0
2	4141	1965	Graduation	Together	71613.0	0	0	21-08-2013	26	426	...	4	3	11	0
3	6182	1984	Graduation	Together	26646.0	1	0	10/2/2014	26	11	...	6	3	11	0
4	5324	1981	PhD	Married	58293.0	1	0	19-01-2014	94	173	...	5	3	11	0

	ID	Year_Birth	Education	Marital_Status	Income	Kidhome	Teenhome	Dt_Customer	Recency	MntWines	...	NumWebVisitsMonth	AcceptedCmp4	AcceptedCmp1	Z_CostContact	Z_Revenue	Response
2235	10870	1967	Graduation	Married	61223.0	0	1	13-06-2013	46	709	...	5	0	0	3	11	0
2236	4001	1946	PhD	Together	64014.0	2	1	10/6/2014	56	406	...	7	0	1	3	11	0
2237	7270	1981	Graduation	Divorced	56981.0	0	0	25-01-2014	91	908	...	6	1	0	3	11	0
2238	8235	1956	Master	Together	69245.0	0	1	24-01-2014	8	428	...	3	0	0	3	11	0
2239	9405	1954	PhD	Married	52869.0	1	1	15-10-2012	40	84	...	7	0	0	3	11	1

Conclusion:¶